






"""simple docstring"""
import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)







class      UpperCamelCase_			(__A	):





	def __init__( self   :					str      ,      lowerCAmelCase_   :					Any=0.0_1      ,      lowerCAmelCase_   :					int=1_000		)		->     List[str]:
				UpperCAmelCase_	:					Tuple     	=   p_stop
				UpperCAmelCase_	:					int     	=   max_length





	def __iter__( self   :					Optional[int]		)		->     Dict:
				UpperCAmelCase_	:					Optional[Any]     	=   0
				UpperCAmelCase_	:					Tuple     	=   False
				while not stop and count < self.max_length:
							yield count
							count += 1
							UpperCAmelCase_	:					Dict     	=   random.random() < self.p_stop







class      UpperCamelCase_			(unittest.TestCase	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					str=False      ,      lowerCAmelCase_   :					Optional[int]=True		)		->     int:
				UpperCAmelCase_	:					List[str]     	=   [
				    BatchSamplerShard(lowerCAmelCase_      ,      2      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)
				    for i in range(2		)
				]
				UpperCAmelCase_	:					Tuple     	=   [list(lowerCAmelCase_		) for batch_sampler_shard in batch_sampler_shards]
				if not split_batches:
							self.assertListEqual([len(lowerCAmelCase_		) for shard in batch_sampler_shards]      ,      [len(lowerCAmelCase_		) for e in expected]		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Union[str, Any]:
				# Check the shards when the dataset is a round multiple of total batch size.
				UpperCAmelCase_	:					List[Any]     	=   BatchSampler(range(24		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   BatchSampler(range(24		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				# Expected shouldn't change
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_		)

				# Check the shards when the dataset is a round multiple of batch size but not total batch size.
				UpperCAmelCase_	:					Optional[int]     	=   BatchSampler(range(21		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					Dict     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Tuple     	=   BatchSampler(range(21		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_		)

				# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
				# num_processes batch.
				UpperCAmelCase_	:					Any     	=   BatchSampler(range(22		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					List[Any]     	=   BatchSampler(range(22		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_		)

				# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
				# num_processes batch.
				UpperCAmelCase_	:					List[Any]     	=   BatchSampler(range(20		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					List[str]     	=   BatchSampler(range(20		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_		)

				# Check the shards when the dataset is very small.
				UpperCAmelCase_	:					Any     	=   BatchSampler(range(2		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   [[[0, 1, 0]], [[1, 0, 1]]]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					List[str]     	=   BatchSampler(range(2		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   [[], []]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Optional[int]:
				# Check the shards when the dataset is a round multiple of batch size.
				UpperCAmelCase_	:					Any     	=   BatchSampler(range(24		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[Any]     	=   BatchSampler(range(24		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				# Expected shouldn't change
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)

				# Check the shards when the dataset is not a round multiple of batch size.
				UpperCAmelCase_	:					Any     	=   BatchSampler(range(22		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[int]     	=   [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)

				UpperCAmelCase_	:					List[str]     	=   BatchSampler(range(22		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)

				# Check the shards when the dataset is not a round multiple of batch size or num_processes.
				UpperCAmelCase_	:					List[str]     	=   BatchSampler(range(21		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   BatchSampler(range(21		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)

				# Check the shards when the dataset is very small.
				UpperCAmelCase_	:					str     	=   BatchSampler(range(2		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   [[[0, 1]], [[0, 1]]]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)

				UpperCAmelCase_	:					Tuple     	=   BatchSampler(range(2		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   [[], []]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Any:
				# Check the shards when the dataset is a round multiple of total batch size.
				UpperCAmelCase_	:					Dict     	=   BatchSampler(range(24		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				UpperCAmelCase_	:					int     	=   BatchSampler(range(24		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				# Expected shouldn't change
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				# Check the shards when the dataset is a round multiple of batch size but not total batch size.
				UpperCAmelCase_	:					Union[str, Any]     	=   BatchSampler(range(21		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				UpperCAmelCase_	:					Tuple     	=   BatchSampler(range(21		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[int]     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
				# num_processes batch.
				UpperCAmelCase_	:					str     	=   BatchSampler(range(22		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				UpperCAmelCase_	:					int     	=   BatchSampler(range(22		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
				# num_processes batch.
				UpperCAmelCase_	:					Tuple     	=   BatchSampler(range(20		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   BatchSampler(range(20		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				# Check the shards when the dataset is very small.
				UpperCAmelCase_	:					Tuple     	=   BatchSampler(range(2		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[int]     	=   [[[0, 1]], []]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   BatchSampler(range(2		)      ,      batch_size=3      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					Any     	=   [[], []]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Optional[int]:
				# Check the shards when the dataset is a round multiple of batch size.
				UpperCAmelCase_	:					Any     	=   BatchSampler(range(24		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[int]     	=   BatchSampler(range(24		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				# Expected shouldn't change
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				# Check the shards when the dataset is not a round multiple of batch size.
				UpperCAmelCase_	:					Any     	=   BatchSampler(range(22		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					Dict     	=   [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[int]     	=   BatchSampler(range(22		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				# Check the shards when the dataset is not a round multiple of batch size or num_processes.
				UpperCAmelCase_	:					Any     	=   BatchSampler(range(21		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				UpperCAmelCase_	:					Tuple     	=   BatchSampler(range(21		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
				]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				# Check the shards when the dataset is very small.
				UpperCAmelCase_	:					Union[str, Any]     	=   BatchSampler(range(2		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   [[[0, 1]], []]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   BatchSampler(range(2		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   [[], []]
				self.check_batch_sampler_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      split_batches=lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Any:
				UpperCAmelCase_	:					Optional[Any]     	=   [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
				UpperCAmelCase_	:					Optional[int]     	=   [BatchSamplerShard(lowerCAmelCase_      ,      2      ,      lowerCAmelCase_      ,      even_batches=lowerCAmelCase_		) for i in range(2		)]

				self.assertEqual(len(batch_sampler_shards[0]		)      ,      3		)
				self.assertEqual(len(batch_sampler_shards[1]		)      ,      2		)

				self.assertListEqual(list(batch_sampler_shards[0]		)      ,      [[0, 1, 2], [5, 6, 7, 8], [12, 13]]		)
				self.assertListEqual(list(batch_sampler_shards[1]		)      ,      [[3, 4], [9, 10, 11]]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Optional[int]=False      ,      lowerCAmelCase_   :					Any=2      ,      lowerCAmelCase_   :					List[Any]=False		)		->     Dict:
				random.seed(lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   list(lowerCAmelCase_		)

				UpperCAmelCase_	:					Any     	=   [
				    IterableDatasetShard(
				        lowerCAmelCase_      ,      batch_size=lowerCAmelCase_      ,      drop_last=lowerCAmelCase_      ,      num_processes=lowerCAmelCase_      ,      process_index=lowerCAmelCase_      ,      split_batches=lowerCAmelCase_      ,      )
				    for i in range(lowerCAmelCase_		)
				]
				UpperCAmelCase_	:					Dict     	=   []
				for iterable_dataset_shard in iterable_dataset_shards:
							# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
							random.seed(lowerCAmelCase_		)
							iterable_dataset_lists.append(list(lowerCAmelCase_		)		)

				UpperCAmelCase_	:					List[Any]     	=   batch_size // num_processes if split_batches else batch_size
				# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
				UpperCAmelCase_	:					str     	=   iterable_dataset_lists[0]
				for l in iterable_dataset_lists[1:]:
							self.assertEqual(len(lowerCAmelCase_		)      ,      len(lowerCAmelCase_		)		)
							self.assertTrue(len(lowerCAmelCase_		) % shard_batch_size == 0		)

				UpperCAmelCase_	:					Optional[int]     	=   []
				for idx in range(0      ,      len(lowerCAmelCase_		)      ,      lowerCAmelCase_		):
							for l in iterable_dataset_lists:
										observed += l[idx : idx + shard_batch_size]

				if not drop_last:
							while len(lowerCAmelCase_		) < len(lowerCAmelCase_		):
										reference += reference
				self.assertListEqual(lowerCAmelCase_      ,      reference[: len(lowerCAmelCase_		)]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     List[str]:
				UpperCAmelCase_	:					Optional[Any]     	=   42
				UpperCAmelCase_	:					Optional[int]     	=   RandomIterableDataset()

				self.check_iterable_dataset_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      batch_size=4      ,      drop_last=lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)
				self.check_iterable_dataset_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      batch_size=4      ,      drop_last=lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)
				self.check_iterable_dataset_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      batch_size=4      ,      drop_last=lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)
				self.check_iterable_dataset_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      batch_size=4      ,      drop_last=lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)

				# Edge case with a very small dataset
				UpperCAmelCase_	:					List[str]     	=   RandomIterableDataset(max_length=2		)

				self.check_iterable_dataset_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      batch_size=4      ,      drop_last=lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)
				self.check_iterable_dataset_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      batch_size=4      ,      drop_last=lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)
				self.check_iterable_dataset_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      batch_size=4      ,      drop_last=lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)
				self.check_iterable_dataset_shards(lowerCAmelCase_      ,      lowerCAmelCase_      ,      batch_size=4      ,      drop_last=lowerCAmelCase_      ,      split_batches=lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Any     	=   BatchSampler(range(16		)      ,      batch_size=4      ,      drop_last=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   SkipBatchSampler(lowerCAmelCase_      ,      2		)
				self.assertListEqual(list(lowerCAmelCase_		)      ,      [[8, 9, 10, 11], [12, 13, 14, 15]]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Any:
				UpperCAmelCase_	:					str     	=   SkipDataLoader(list(range(16		)		)      ,      batch_size=4      ,      skip_batches=2		)
				self.assertListEqual([t.tolist() for t in dataloader]      ,      [[8, 9, 10, 11], [12, 13, 14, 15]]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     List[Any]:
				UpperCAmelCase_	:					List[str]     	=   DataLoader(list(range(16		)		)      ,      batch_size=4		)
				UpperCAmelCase_	:					int     	=   skip_first_batches(lowerCAmelCase_      ,      num_batches=2		)
				self.assertListEqual([t.tolist() for t in new_dataloader]      ,      [[8, 9, 10, 11], [12, 13, 14, 15]]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     str:
				UpperCAmelCase_	:					List[Any]     	=   DataLoaderShard(list(range(16		)		)      ,      batch_size=4		)
				for idx, _ in enumerate(lowerCAmelCase_		):
							self.assertEqual(dataloader.end_of_dataloader      ,      idx == 3		)

				# Test it also works on the second iteration
				for idx, _ in enumerate(lowerCAmelCase_		):
							self.assertEqual(dataloader.end_of_dataloader      ,      idx == 3		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     int:
				Accelerator()
				UpperCAmelCase_	:					Optional[Any]     	=   DataLoaderDispatcher(range(16		)      ,      batch_size=4		)
				for idx, _ in enumerate(lowerCAmelCase_		):
							self.assertEqual(dataloader.end_of_dataloader      ,      idx == 3		)

				# Test it also works on the second iteration
				for idx, _ in enumerate(lowerCAmelCase_		):
							self.assertEqual(dataloader.end_of_dataloader      ,      idx == 3		)




"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase							)				->   "list[int]":




						'''simple docstring'''



						if upper_limit < 0:
												raise ValueError("Limit for the Catalan sequence must be ≥ 0"							)

						lowerCamelCase__   =[0] * (upper_limit + 1)

						# Base case: C(0) = C(1) = 1
						lowerCamelCase__   =1
						if upper_limit > 0:
												lowerCamelCase__   =1

						# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
						for i in range(2  ,       upper_limit + 1							):
												for j in range(__lowerCAmelCase							):
																		catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

						return catalan_list


if __name__ == "__main__":
	print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
	print('\n*** Enter -1 at any time to quit ***')
	print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
	try:
		while True:
			a				=int(input().strip())
			if N < 0:
				print('\n********* Goodbye!! ************')
				break
			else:
				print(F'''The Catalan numbers from 0 through {N} are:''')
				print(catalan_numbers(N))
				print('Try another upper limit for the sequence: ', end='')
	except (NameError, ValueError):
		print('\n********* Invalid input, goodbye! ************\n')

	import doctest

	doctest.testmod()


